A.I, Data and Software Engineering
As PCA and linear autoencoder have a close relation, this post introduces again PCA as a powerful dimension reduction tool while skipping many mathematical proofs. PCA is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly...
This post introduces using linear autoencoder for dimensionality reduction using TensorFlow and Keras. What is a linear autoencoder An autoencoder is a type of artificial neural network used to learn efficient data codings in an unsupervised manner. The aim of an autoencoder is to learn a representation (encoding) for a set of data, typically for dimensionality reduction, by training the network...
A.I, Data and Software Engineering